Extract text from any PDF, one request.

Multi-column academic papers, dense financial tables, scanned contracts — one endpoint, one response shape, no per-layout tuning.

the-problem

PDFs are the least standardized 'standard' in document formats: multi-column layouts that confuse naive text extraction, tables that collapse into unreadable strings, and scanned pages with no text layer at all. Most teams end up bolting together a PDF library for the easy cases and a separate OCR pipeline for the scanned ones — two code paths to maintain, two sets of edge cases to debug. VLM-based extractors that read pages as images tend to do fine on a single clean scan but struggle with mixed-format batches — a folder with digital-native reports next to faxed scans breaks the assumption that every page is a picture.

one-request-solution

txtfetch takes the PDF — digital-native or scanned, doesn't matter — and always gives back the same JSON shape. Text-layer pages go straight through Apache Tika; pages with no text layer route through Tesseract OCR automatically, in the same request. One code path for every PDF in your pipeline.

curl
curl -X POST https://api.txtfetch.com/v1/extract \
  -H "Authorization: Bearer $TXTFETCH_KEY" \
  -F file=@quarterly-report.pdf
Python
import os
import requests

with open("quarterly-report.pdf", "rb") as f:
    r = requests.post(
        "https://api.txtfetch.com/v1/extract",
        headers={"Authorization": f"Bearer {os.environ['TXTFETCH_KEY']}"},
        files={"file": f},
    )

print(r.json()["extracted_text"])
JavaScript
import { readFile } from "node:fs/promises";

const file = new Blob([await readFile("quarterly-report.pdf")]);
const form = new FormData();
form.append("file", file, "quarterly-report.pdf");

const res = await fetch("https://api.txtfetch.com/v1/extract", {
  method: "POST",
  headers: { Authorization: `Bearer ${process.env.TXTFETCH_KEY}` },
  body: form,
});

const { extracted_text } = await res.json();
console.log(extracted_text);
Go
package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"io"
	"mime/multipart"
	"net/http"
	"os"
)

type extractResponse struct {
	Status        string `json:"status"`
	ExtractedText string `json:"extracted_text"`
}

func main() {
	f, err := os.Open("quarterly-report.pdf")
	if err != nil {
		panic(err)
	}
	defer f.Close()

	var body bytes.Buffer
	writer := multipart.NewWriter(&body)
	part, err := writer.CreateFormFile("file", "quarterly-report.pdf")
	if err != nil {
		panic(err)
	}
	if _, err := io.Copy(part, f); err != nil {
		panic(err)
	}
	writer.Close()

	req, err := http.NewRequest("POST", "https://api.txtfetch.com/v1/extract", &body)
	if err != nil {
		panic(err)
	}
	req.Header.Set("Authorization", "Bearer "+os.Getenv("TXTFETCH_KEY"))
	req.Header.Set("Content-Type", writer.FormDataContentType())

	resp, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer resp.Body.Close()

	var result extractResponse
	if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
		panic(err)
	}
	fmt.Println(result.ExtractedText)
}

Or skip the download — pass a url parameter and txtfetch fetches the document server-side:

curl
curl -X POST "https://api.txtfetch.com/v1/extract?url=https://example.com/whitepaper.pdf" \
  -H "Authorization: Bearer $TXTFETCH_KEY"
Python
import os
import requests

r = requests.post(
    "https://api.txtfetch.com/v1/extract",
    headers={"Authorization": f"Bearer {os.environ['TXTFETCH_KEY']}"},
    params={"url": "https://example.com/whitepaper.pdf"},
)

print(r.json()["extracted_text"])
JavaScript
const endpoint = new URL("https://api.txtfetch.com/v1/extract");
endpoint.searchParams.set("url", "https://example.com/whitepaper.pdf");

const res = await fetch(endpoint, {
  method: "POST",
  headers: { Authorization: `Bearer ${process.env.TXTFETCH_KEY}` },
});

const { extracted_text } = await res.json();
console.log(extracted_text);
Go
package main

import (
	"encoding/json"
	"fmt"
	"net/http"
	"net/url"
	"os"
)

type extractResponse struct {
	Status        string `json:"status"`
	ExtractedText string `json:"extracted_text"`
}

func main() {
	endpoint, err := url.Parse("https://api.txtfetch.com/v1/extract")
	if err != nil {
		panic(err)
	}
	q := endpoint.Query()
	q.Set("url", "https://example.com/whitepaper.pdf")
	endpoint.RawQuery = q.Encode()

	req, err := http.NewRequest("POST", endpoint.String(), nil)
	if err != nil {
		panic(err)
	}
	req.Header.Set("Authorization", "Bearer "+os.Getenv("TXTFETCH_KEY"))

	resp, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer resp.Body.Close()

	var result extractResponse
	if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
		panic(err)
	}
	fmt.Println(result.ExtractedText)
}
{
  "status": "success",
  "extracted_text": "..."
}

formats-covered

  • .pdf

faq

How do I extract text from a PDF?
POST the file as multipart form data to https://api.txtfetch.com/v1/extract, or pass a url parameter and txtfetch fetches it server-side. Either way you get back { "status": "success", "extracted_text": "..." }.
Does it handle scanned PDFs, not just digital-native ones?
Yes. Pages with no text layer route through Tesseract OCR automatically — same request, same response shape. You don't need to detect or flag scanned pages yourself.
What about multi-column layouts and tables?
Apache Tika parses the underlying PDF structure rather than guessing from pixel positions, so multi-column academic papers and tabular financial reports come out as readable, ordered text.
Is there a page limit?
No. One extraction request is one document, regardless of length — a 300-page PDF still counts as a single request.

go-further

PDF & scans

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